Chip-Based Diagnosis of Problematic Liver Tumors

描述（由申请人提供）：精确的肿瘤诊断和分类是癌症管理的第一步。虽然许多肿瘤活检是诊断性的并且形成癌症治疗的基石，但是肿瘤类型和起源部位的分类是始终存在的临床挑战。据估计，高达10%的肿瘤没有明确的原发部位，每个病例花费数千美元来确定原发部位，但总体成功率有限。目前的病理学标准，使用形态学标准和一组半定量免疫组化（IHC）分析，往往是有限的，在其能力，以确定肿瘤类型或起源部位。此外，当原发部位未确定时（原发癌未知），转移性病变的诊断可能相当困难。由于治疗通常基于起源部位，因此明确需要识别和验证分类器，该分类器将清楚地区分这些组织学上相似的肿瘤类型，并在进行诊断呼叫时增强标准病理学技术。我们最近证明了使用基因表达谱来区分21种不同肿瘤类型的可行性，准确率为88%。然而，该分类器的性能主要受限于使用多个平台进行分析。该提案旨在建立一个新的基因表达分类器在一个单一的，商业上可获得的，全基因组的寡核苷酸平台，重点是最有问题的原发性和转移性肿瘤的肝脏。为了证明这种方法的临床实用性，我们计划用独立的测试集来验证分类器，这些测试集也将通过标准IHC方法进行分析;然后将分子分类的结果与标准病理分类进行头对头比较，以获得诊断的准确性。为了进一步转化该技术，我们将选择核心分类器基因并使用真实的时间定量PCR进行验证。最后，将使用前瞻性采集的已知和未知来源部位组织的活检样本对分子分类器进行测试。

DESCRIPTION (provided by applicant): Precise tumor diagnosis and classification is the first step in cancer management. While many tumor biopsies are diagnostic and form the cornerstone of cancer therapy, classification of tumor type and site of origin, is an ever-present clinical challenge. It is estimated that up to 10% of all tumors have no defined primary site of origin and that thousands of dollars are spent per case to identify the site of origin with limited overall success. The current standard of pathologic practice, using morphologic criteria and a panel of semi-quantitative immunohistochemical (IHC) analyses, is often limited in its capacity to define tumor type or site of origin. Moreover, the diagnosis of metastatic lesions can be quite difficult when no primary site of origin has been identified (unknown primary cancers). Since therapy is often based on site of origin, there is a clear need for the identification and validation of a classifier that will cleanly distinguish these histologically similar tumor types and augment standard pathological techniques in making the diagnostic call. We have recently demonstrated the feasibility of using gene expression profiling to discriminate 21 different tumor types with an accuracy of 88%. The performance of this classifier, however, was principally limited by the use of multiple platforms for analysis. This proposal seeks to build a new gene expression classifier on a single, commercially available, genome-wide oligonucleotide platform, with a focus on the most problematic primary and metastatic tumors of the liver. To demonstrate the clinical utility of this approach, we plan to validate the classifier with independent test sets that will also be profiled by standard IHC approaches; the results of molecular classification will then be compared head to head with standard pathological classification for accuracy of diagnosis. To further translate the technology, we will select core classifier genes and perform validation with real time quantitative PCR. Finally, the molecular classifiers will be tested using prospectively acquired biopsy samples on tissues of known and unknown sites of origin.